Abstract
This paper explores the experiment of Deep Learning method using Mask Region-Convolutional Neural Network (Mask R-CNN) to identify rock-forming minerals on thin section images from petrographic observation in igneous rocks, which are plagioclase, quartz, K-feldspar, pyroxene, and hornblende. Train and validation dataset consisted of 2 quartz diorites and 1 granodiorite from Monterado, West Kalimantan, 1 quartz diorite and 1 granite from Nangapinoh, West Kalimantan, and 7 andesite and 2 basalts from Bangli, Bali, while test dataset consisted of 3 quartz diorites from Monterado, West Kalimantan. This study uses 4 Mask R-CNN models, which is influenced by the lighting on polarizing microscope and using ResNet-50 architecture (Model A) or ResNet-101 (Model B), and the models that is not affected by the lighting on polarizing microscope and using ResNet-50 architecture (Model C) or ResNet-101 (Model D). From Average Precision scores, it was found that Model B has the highest score (58.0%), followed by Model A (57.8%), Model C (45.8%), and Model D (43.6%). In conclusion, the lighting of polarizing microscope is a major factor to give a better performances of Mask R-CNN models by 12%-14.4%, while the type of backbone architecture on Mask R-CNN models was not too consequential.
Highlights
Rock classification is an important work for a geologist, and could be achieved through field observations of handspecimen samples, as well as observations in the laboratory using a petrographic microscope, X-Ray Diffraction (XRD), or X-Ray Fluorescence (XRF)
The label of annotations based on the effect of lighting on polarizing microscope are: 1. ‘pl’, ‘px’, ‘kfs’, ‘hb’, and ‘qz’, for dataset that are affected by the lighting on polarizing microscope, performed by combining mineral labels on Plane Polarized Light (PPL) and XPL appearance (‘Dataset 1’). 2. ‘pl_ppl’, ‘pl_xpl’, ‘px_ppl’, ‘px_xpl’, ‘kfs_ppl’, ‘kfs_xpl’, ‘hb_ppl’, ‘hb_xpl’, ‘qz_ppl’, and ‘qz_xpl’, for dataset that are not affected by the lighting on polarizing microscope, performed by separating mineral labels on PPL and XPL (‘Dataset 2’)
As shown above at diagram of validation loss/accuracy (Figure 2), and bar chart of Average Precision (Figure 4), we reveal that microscope and using ResNet-50 architecture (Model A) and Model B are the top two models, which
Summary
Rock classification is an important work for a geologist, and could be achieved through field observations of handspecimen samples, as well as observations in the laboratory using a petrographic microscope, X-Ray Diffraction (XRD), or X-Ray Fluorescence (XRF). Petrographic observations is the most commonly used research method because it considered more effective and efficient. This method often takes a lot of time and sometimes has a high error rate, an artificial intelligence system under human supervision would be required to automate petrographic mineral identification in rock classification [1]
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